NMF-SVM Based CAD Tool Applied to Functional Brain Images for the Diagnosis of Alzheimer's Disease

Abstract

This paper presents a novel computer-aided diagnosis (CAD) technique for the early diagnosis of the Alzheimer's disease (AD) based on nonnegative matrix factorization (NMF) and support vector machines (SVM) with bounds of confidence. The CAD tool is designed for the study and classification of functional brain images. For this purpose, two different brain image databases are selected: a single photon emission computed tomography (SPECT) database and positron emission tomography (PET) images, both of them containing data for both Alzheimer's disease (AD) patients and healthy controls as a reference. These databases are analyzed by applying the Fisher discriminant ratio (FDR) and nonnegative matrix factorization (NMF) for feature selection and extraction of the most relevant features. The resulting NMF-transformed sets of data, which contain a reduced number of features, are classified by means of a SVM-based classifier with bounds of confidence for decision. The proposed NMF-SVM method yields up to 91% classification accuracy with high sensitivity and specificity rates (upper than 90%). This NMF-SVM CAD tool becomes an accurate method for SPECT and PET AD image classification.

Characterization of brain anatomical patterns by comparing region intensity distributions: Applications to the description of Alzheimer's disease

“…The use of SVM in neuroimaging research has been reported in multiple (Cuingnet, Chupin, Benali, & Colliot, 2010; Cuingnet et al., 2011; Kloppel et al., 2008; Magnin et al., 2009; Padilla et al., 2012; Tanoori, Azimifar, Shakibafar, & Katebi, 2011; Zhang, Shen, & Alzheimer's Disease Neuroimaging Initiative, 2012). …”

The Role of Neuroimaging and Electroencephalogram in Diagnosis of Alzheimer Disease

“…As previously stated CAD is a general tool used for a variety of application such as to diagnose the disease in medical applications. CAD helps the physicians, researchers in diagnosing the disease in less time by identifying he patterns, making fewer efforts [11].…”

Classification of Alzheimer Disease based on Normalized Hu Moment Invariants and Multiclassifier

“…It is suitable for high dimensional classification problems, where not too many examples exist. SVM has been utilized, for instance, in [8] for classifying MR images and in [9], [10] to classify Position Emission Tomography (PET) images. All these works used voxel intensity (VI) as features.…”